Genomic Functional Regression analysis in R | Development version 0.9.13 [Last README update: 2018-05-22] Table of contents News of this version (0.9.13) Revision 13 The BFR function now can obtain the PCC (Percentage of Correct Classification) to the Ordinal or binary data (BFR(..., re...
Wavelet-based functional regression methods with scalar responses and functional predictors can be found in the wcr and wnet functions in the refund.wave package. Installation To install the latest patched version directly from Github, please use devtools::install_github("refunders/refund") for refun...
Regression Transformer: concurrent sequence regression and generation for molecular language modeling. Nat. Mach. Intell. 5, 432–444 (2023). Google Scholar Notin, P., Weitzman, R., Marks, D. S. & Gal, Y. ProteinNPT: improving protein property prediction and design with non-parametric ...
For example, we investigated stratified subgroups rather than regression in line with previous ENIGMA-OCD mega-analyses. Additionally, we used a mega-analytic approach by pooling individual data across studies rather than a meta-analytic approach that synthesizes summary statistics to estimate an ...
A Multivariate Adaptive Regression Splines model for determining horizontal wall deflection envelope for braced excavations in clays. Tunn. Undergr. Space Technol. 84, 461-471 [71] Zhang, W., Zhang, R., Wu, C., Goh, A.T., Wang, L., 2020b. Assessment of basal heave stability for ...
Leakages of cold, methane-rich fluids from subsurface reservoirs to the sea floor are termed cold seeps. Recent exploration of the deep sea has shed new light on the microbial communities in cold seeps. However, conventional metagenomic methods largely rely on reference databases and neglect the ph...
The correlation between cone outer segment morphology and cone function is even more apparent when the data of Figure 5i are plotted with those of Figure 4e, the data points then align in a strength lane with a regression R of 0.8939. To get more details on the morphology of the cones, ...
The AME are a variation on a multinomial logistic regression, and show the association between a certain characteristic (i.e. age) and a trajectory for a one-unit change of that characteristic. The AME’s main advantage over the estimates of multinomial logistic regression lies in the fact ...
and the supervisor labels the data41. Our application is in the regression and prediction area rather than the labelling area. Our supervised learning scheme is closer to the active learning method developed by Zhang et al.42for neural net modelling of force fields, but with some differences be...
(8) de-meaning and removal of any linear trends; (9) regression of the 24 motion parameters, including six framewise estimates of motion, the derivatives of each of these six parameters, and quadratic terms of each of the six parameters and their derivatives; and (10) bandpass filtering ...